Marie Curie Fellow
Systems immunologist applying machine learning to understand how vaccines work, specifically focused on human immunology and data-driven research. Co-developer of SIMON, an open source platform for the application of machine learning to biological and clinical data.
UTILIZING SYSTEMS IMMUNOLOGY TO UNDERSTAND PROTECTIVE IMMUNITY
Marie Curie Fellow working in close collaboration between Stanford University and the University of Oxford to identify protective immunity against influenza in children using a systems immunology approach. From 2017 to 2018, as a postdoctoral fellow in the group of prof. dr. Mark M. Davis at the Institute of Immunity, Transplantation and Infection at Stanford University in the USA developed a computational tool for the application of machine learning to clinical datasets. During PhD generated recombinant cytomegalovirus as a vaccine candidate at the Institute for Virology at the Hannover Medical School, under the supervision of prof. dr. Martin Messerle, and received the MHH Award for the best PhD work in 2017.
My research focus is to understand the immunogenicity of the live attenuated influenza vaccine in children using systems immunology approach and SIMON, our recently developed machine learning tool. This approach holds promise to reveal the influenza vaccine imprint (FluPRINT) on immune system. If you want to learn more, please check out the website dedicated to the project at fluprint.com. If you are interested to know more about SIMON, our machine learning pipeline and take part in the open source society dedicated to make SIMON free for everyone please visit genular.com.
The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.
Tomic A. et al, (2019), Sci Data, 6